Equilibrium propagation[1]} is a new framework for machine learning, where the prediction and the objective function are defined implicitly through an energy function of the data and the parameters of the model, rather than explicitly as in a feedforward net. The energy function \(F(\theta ,\beta ,s,v)\)  is defined to model all interactions within the system and the actions with the external world of the system, where \(\theta \)  is the parameter to be learned, \(\beta \)  is a parameter to control the level of the influence of the external world, \(v\)  is the state of the external world(input and expected output) and \(s\)  is the state of the system.
